Difference-based UDA alleviates domain differences by minimizing statistical differences. [1]} minimized the maximum mean difference for task-specific layers to explicitly narrow the domain gap. [2]} introduced joint maximum mean difference to enforce joint distribution alignment between domains. [3]} and [4]} minimized inter-domain differences by aligning the second-order statistics of the source and target distributions. Based on the optimal transfer distance, [5]}, [6]}, [7]}, [8]} designed optimal transfer models to perform feature alignment in source and target domains. Regularizes such as entropy constraints [9]} or maximum prediction rank [10]} can be used to implicitly constrain the cross-domain feature space. Although such methods help to narrow the distance of similar features between domains, due to the lack of labels in the target domain, the feature extractor may incorrectly draw the distance of similar but non-same-class features, resulting in a decrease in discriminability.
